[
https://issues.apache.org/jira/browse/SPARK-21797?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16140261#comment-16140261
]
Boris Clémençon commented on SPARK-21797:
------------------------------------------
Steve,
This is the stacks:
{noformat}
WARN TaskSetManager: Lost task 0.1 in stage 0.0 (TID 1,
ip-172-31-42-242.eu-west-1.compute.internal, executor 1): java.io.IOException:
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.model.AmazonS3Exception:
The operation is not valid for the object's storage class (Service: Amazon S3;
Status Code: 403; Error Code: InvalidObjectState; Request ID:
5DD5BEBB8173977D), S3 Extended Request ID:
K9bDwhm32CFHeg5zgVfW/T1A/vB4e8gqQ/p7E0Ze9ZG55UFoDP7hgnkQxLIwYX9i2LEcKwrR+lo=
at
com.amazon.ws.emr.hadoop.fs.s3n.Jets3tNativeFileSystemStore.handleAmazonServiceException(Jets3tNativeFileSystemStore.java:434)
at
com.amazon.ws.emr.hadoop.fs.s3n.Jets3tNativeFileSystemStore.retrievePair(Jets3tNativeFileSystemStore.java:461)
at
com.amazon.ws.emr.hadoop.fs.s3n.Jets3tNativeFileSystemStore.retrievePair(Jets3tNativeFileSystemStore.java:439)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at
org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:191)
at
org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
at com.sun.proxy.$Proxy30.retrievePair(Unknown Source)
at
com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem.open(S3NativeFileSystem.java:1201)
at org.apache.hadoop.fs.FileSystem.open(FileSystem.java:773)
at
com.amazon.ws.emr.hadoop.fs.EmrFileSystem.open(EmrFileSystem.java:166)
at
org.apache.parquet.hadoop.util.HadoopInputFile.newStream(HadoopInputFile.java:65)
at
org.apache.parquet.hadoop.ParquetFileReader.readFooter(ParquetFileReader.java:443)
at
org.apache.parquet.hadoop.ParquetFileReader.readFooter(ParquetFileReader.java:421)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anonfun$readParquetFootersInParallel$1.apply(ParquetFileFormat.scala:491)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anonfun$readParquetFootersInParallel$1.apply(ParquetFileFormat.scala:485)
at
scala.collection.parallel.AugmentedIterableIterator$class.flatmap2combiner(RemainsIterator.scala:132)
at
scala.collection.parallel.immutable.ParVector$ParVectorIterator.flatmap2combiner(ParVector.scala:62)
at
scala.collection.parallel.ParIterableLike$FlatMap.leaf(ParIterableLike.scala:1072)
at
scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply$mcV$sp(Tasks.scala:49)
at
scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:48)
at
scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:48)
at scala.collection.parallel.Task$class.tryLeaf(Tasks.scala:51)
at
scala.collection.parallel.ParIterableLike$FlatMap.tryLeaf(ParIterableLike.scala:1068)
at
scala.collection.parallel.AdaptiveWorkStealingTasks$WrappedTask$class.compute(Tasks.scala:152)
at
scala.collection.parallel.AdaptiveWorkStealingForkJoinTasks$WrappedTask.compute(Tasks.scala:443)
at
scala.concurrent.forkjoin.RecursiveAction.exec(RecursiveAction.java:160)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
at scala.concurrent.forkjoin.ForkJoinTask.doJoin(ForkJoinTask.java:341)
at scala.concurrent.forkjoin.ForkJoinTask.join(ForkJoinTask.java:673)
at
scala.collection.parallel.ForkJoinTasks$WrappedTask$class.sync(Tasks.scala:378)
at
scala.collection.parallel.AdaptiveWorkStealingForkJoinTasks$WrappedTask.sync(Tasks.scala:443)
at
scala.collection.parallel.ForkJoinTasks$class.executeAndWaitResult(Tasks.scala:426)
at
scala.collection.parallel.ForkJoinTaskSupport.executeAndWaitResult(TaskSupport.scala:56)
at
scala.collection.parallel.ParIterableLike$ResultMapping.leaf(ParIterableLike.scala:958)
at
scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply$mcV$sp(Tasks.scala:49)
at
scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:48)
at
scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:48)
at scala.collection.parallel.Task$class.tryLeaf(Tasks.scala:51)
at
scala.collection.parallel.ParIterableLike$ResultMapping.tryLeaf(ParIterableLike.scala:953)
at
scala.collection.parallel.AdaptiveWorkStealingTasks$WrappedTask$class.compute(Tasks.scala:152)
at
scala.collection.parallel.AdaptiveWorkStealingForkJoinTasks$WrappedTask.compute(Tasks.scala:443)
at
scala.concurrent.forkjoin.RecursiveAction.exec(RecursiveAction.java:160)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
at
scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
at
scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
at
scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
Caused by:
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.model.AmazonS3Exception:
The operation is not valid for the object's storage class (Service: Amazon S3;
Status Code: 403; Error Code: InvalidObjectState; Request ID:
5DD5BEBB8173977D), S3 Extended Request ID:
K9bDwhm32CFHeg5zgVfW/T1A/vB4e8gqQ/p7E0Ze9ZG55UFoDP7hgnkQxLIwYX9i2LEcKwrR+lo=
at
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.handleErrorResponse(AmazonHttpClient.java:1588)
at
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeOneRequest(AmazonHttpClient.java:1258)
at
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeHelper(AmazonHttpClient.java:1030)
at
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.doExecute(AmazonHttpClient.java:742)
at
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeWithTimer(AmazonHttpClient.java:716)
at
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.execute(AmazonHttpClient.java:699)
at
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.access$500(AmazonHttpClient.java:667)
at
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutionBuilderImpl.execute(AmazonHttpClient.java:649)
at
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:513)
at
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:4169)
at
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:4116)
at
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.AmazonS3Client.getObject(AmazonS3Client.java:1365)
at
com.amazon.ws.emr.hadoop.fs.s3.lite.call.GetObjectCall.perform(GetObjectCall.java:23)
at
com.amazon.ws.emr.hadoop.fs.s3.lite.call.GetObjectCall.perform(GetObjectCall.java:10)
at
com.amazon.ws.emr.hadoop.fs.s3.lite.executor.GlobalS3Executor.execute(GlobalS3Executor.java:82)
at
com.amazon.ws.emr.hadoop.fs.s3.lite.AmazonS3LiteClient.invoke(AmazonS3LiteClient.java:176)
at
com.amazon.ws.emr.hadoop.fs.s3.lite.AmazonS3LiteClient.getObject(AmazonS3LiteClient.java:99)
at
com.amazon.ws.emr.hadoop.fs.s3n.Jets3tNativeFileSystemStore.retrievePair(Jets3tNativeFileSystemStore.java:452)
... 47 more
{noformat}
> spark cannot read partitioned data in S3 that are partly in glacier
> -------------------------------------------------------------------
>
> Key: SPARK-21797
> URL: https://issues.apache.org/jira/browse/SPARK-21797
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.2.0
> Reporter: Boris Clémençon
> Labels: glacier, partitions, read, s3
>
> I have a dataset in parquet in S3 partitioned by date (dt) with oldest date
> stored in AWS Glacier to save some money. For instance, we have...
> {noformat}
> s3://my-bucket/my-dataset/dt=2017-07-01/ [in glacier]
> ...
> s3://my-bucket/my-dataset/dt=2017-07-09/ [in glacier]
> s3://my-bucket/my-dataset/dt=2017-07-10/ [not in glacier]
> ...
> s3://my-bucket/my-dataset/dt=2017-07-24/ [not in glacier]
> {noformat}
> I want to read this dataset, but only a subset of date that are not yet in
> glacier, eg:
> {code:java}
> val from = "2017-07-15"
> val to = "2017-08-24"
> val path = "s3://my-bucket/my-dataset/"
> val X = spark.read.parquet(path).where(col("dt").between(from, to))
> {code}
> Unfortunately, I have the exception
> {noformat}
> java.io.IOException:
> com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.model.AmazonS3Exception:
> The operation is not valid for the object's storage class (Service: Amazon
> S3; Status Code: 403; Error Code: InvalidObjectState; Request ID:
> C444D508B6042138)
> {noformat}
> I seems that spark does not like partitioned dataset when some partitions are
> in Glacier. I could always read specifically each date, add the column with
> current date and reduce(_ union _) at the end, but not pretty and it should
> not be necessary.
> Is there any tip to read available data in the datastore even with old data
> in glacier?
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
